(Semi)External Algorithms for Graph Partitioning and Clustering
Abstract
In this paper, we develop semiexternal and external memory algorithms for graph partitioning and clustering problems. Graph partitioning and clustering are key tools for processing and analyzing large complex networks. We address both problems in the (semi)external model by adapting the sizeconstrained label propagation technique. Our (semi)external sizeconstrained label propagation algorithm can be used to compute graph clusterings and is a prerequisite for the (semi)external graph partitioning algorithm. The algorithm is then used for both the coarsening and the refinement phase of a multilevel algorithm to compute graph partitions. Our algorithm is able to partition and cluster huge complex networks with billions of edges on cheap commodity machines. Experiments demonstrate that the semiexternal graph partitioning algorithm is scalable and can compute high quality partitions in time that is comparable to the running time of an efficient internal memory implementation. A parallelization of the algorithm in the semiexternal model further reduces running time.
 Publication:

arXiv eprints
 Pub Date:
 April 2014
 arXiv:
 arXiv:1404.4887
 Bibcode:
 2014arXiv1404.4887A
 Keywords:

 Computer Science  Data Structures and Algorithms;
 Computer Science  Social and Information Networks